Transcriptomics

Question 1

What is transcriptomics?

Answer 1

• systematic analysis of transcripts
  
  • length of RNA or DNA that has been transcribed respectively from a DNA or RNA template
  • identify the genes that are active in a particular condition
  • measure the gene level of expression
• Two main approaches:
  
  • EST sequencing (Expressed Sequence Tags)
  • Full length sequencing

Question 2

EST sequencing

Answer 2

• also called single pass sequencing
  
  • resulting sequences obtained by a single read
  • no confirmation of correctness
• random sequencing of cDNA libraries
• allowed to discover many transcripts or fragment of transcripts
• often poor quality of reads
• from the number of reads aligned it was possible to estimate the expression level of all the genes

Question 3

Full length sequencing

Answer 3

• more difficult than EST sequencing
• full length sequence of that transcript
  
  • able to infer the full length of the protein
  • make hypothesis on its function

Question 4

Microarrays

Answer 4

• method to assess expression profiles
  
  • level of expression of all the genes in a single experiment
• using a sequence of nucleic acids as a probe to identify and quantify the complementary strand
• microarray composed of a matrix of micro probes
  
  • cDNA is labelled with fluorescent dyes
  • sequence of each spot in known (possible to quantify level)
• after hybridization the microarray can be analyzed by a laser scanner

Question 5

What are expression profiles?

Answer 5

• indicates overall level of expression of all the genes
• an expression profile is an “object” defined by a multi-variable vector
• each gene is an independent
  
  • if all variables have the same value then the two profiles are identical
• we can measure the distance
  
  • of two profiles -> euclidean distance
  • many profiles -> clustering (unsupervised, supervised, hierarchical)

Question 6

Hierarchical clustering

Answer 6

• method of cluster analysis which seeks to build a
  hierarchy of clusters
• Two main types:
  
  • agglomerative, bottom-up, single clusters merged, O(n^3)
  • divisive, top-down, whole set divided, O(2^n)
• merges and splits greedy manner
• results in dendrogram
• given complexity other efficien algorithms used (slink, clink) O(n^2)

Question 7

unsupervised and supervised clustering

Answer 7

• unsupervised -> find hidden structure in unlabeled data
  
  • no error to evaluate
• supervised -> find function from labeled data
  
  • pair sample-label
  • generalize to unseen instances
• SVM, cluter algorithms, kernel tricks

Question 8

RNA-seq analyses, why?

Answer 8

• discover expressed genes in different tissues or conditions
  
  • gene prediction and functional analysis
• comparison is very often the aim
• two main approaches:
  
  • search for individual genes differentially expressed
  • evaluate the whole expression profile

Question 9

Can we discriminate which strand is transcribed ?

Answer 9

• we loose information about which one contains the sequence and which the complement
• directional cloning, method developed by LifeTechnologies
  
  • RNA Ligase 2
  • sticky adaptors

Question 10

What is Agilent Bioanalyzer?

Answer 10

• a system performing fast and accurate automated electrophoresis
  
  • “on chip” microelectrophoresis
• ribosomal RNA peaks should be very sharp showing very little degradation
• gives RIN (RNA integrity number), estimate of the quality

Question 11

Affinity purification of polyA+ with magnetic beads

Answer 11

• interested only in mRNA<4%, remove the rest

* after it is good practice to run another Bioanalyzer

Question 12

Covaris physical fragmentation of nucleic acids

Answer 12

• libraries must contain short inserts
  
  • emulsion PCR and bridge PCR cannot process long fragments
• used to fragment the DNA (with sonification)

Question 13

RNA fragmentation by Rnase III

Answer 13

• libraries must contain short inserts
  
  • emulsion PCR and bridge PCR cannot process long fragments
• used to fragment the DNA (with enzymatic endonuclease digestion)
  
  • enzyme RNase III

Question 14

What are the e main advantages of RNA-seq?

Answer 14

• single-base resolution (unknown genes or exons)
  
  • Differential splicing
  • Gene prediction
  • RNA editing
• direct measurement of the number of molecules
  
  • microarrays give approximate values

Question 15

From RNA-Seq reads to transcripts

Answer 15

• align-then-assemble approach
  
  • aligns reads to the genome
  • identify splicing events
  • reconstructs transcripts from spliced alignments
• assemble-then-align approach
  
  • assembles transcript sequences from reads [de-novo]
  • transcripts are splice-aligned to the genome
  • delineate intron and exon structures and variations between transcripts
  • more sensitive, de novo works well for most abundant transcripts
• reads colored according to the transcript isoform
  
  • protein dark colours

Question 16

How to compare data from different experiments?

Answer 16

• FPKM, fragments per kilo-base of transcript per million mapped reads
  
  • data normalized on total reads and lenght of transcripts
• normalization on the total number of reads non always ok
• normalization on the length of the transcript non always ok

Question 17

RNA editing

Answer 17

• change some bases on the RNA (by specific enzymes)

* RNA-seq data may be very useful for a direct and global comparison between genome and transcriptome sequences

Question 18

What is RNA-seq?

Answer 18

• RNA sequencing, also called whole transcriptome shotgun
  sequencing (WTSS)
• uses NGS to detect presence and quantity of RNA
• RNAseq has replaced microarrays for transcriptome
  analyses because is more accurate
  
  • easily produce million of reads per sample